1. Technical Field
The present invention relates to category classification methods.
2. Related Art
Conventionally, classification methods that use support vector machines (for example, see JP-A-2005-134966) are known as classification methods for categories such as scenes or the like of images. With support vector machines, support vectors that contribute to classification boundaries are obtained through learning using samples for learning. And discriminants are calculated through computations based on relationships between classification targets and each support vector. By comparing values of the discriminants and threshold values, classification is carried out as to whether or not a classification target pertains to a specific category.
In classification processing using support vector machines, the accuracy of classification can be improved by using more learning samples, that is, by using a greater number of support vectors. In this regard, as is described later, the time for calculating discriminants according to support vector machines is proportional to the number of support vectors. That is, when the number of support vectors is increased to improve the accuracy of classification, the time required to calculate the discriminants increases, thereby reducing the speed of classification processing. Thus, conventionally, there is a problem in that it has been difficult to improve the speed of classification processing.